


'''simple docstring'''



import os
import string
import sys


__A						=1 << 8

__A						={
    'tab': ord('\t'),
    'newline': ord('\r'),
    'esc': 27,
    'up': 65 + ARROW_KEY_FLAG,
    'down': 66 + ARROW_KEY_FLAG,
    'right': 67 + ARROW_KEY_FLAG,
    'left': 68 + ARROW_KEY_FLAG,
    'mod_int': 91,
    'undefined': sys.maxsize,
    'interrupt': 3,
    'insert': 50,
    'delete': 51,
    'pg_up': 53,
    'pg_down': 54,
}

__A						=KEYMAP['up']
__A						=KEYMAP['left']

if sys.platform == "win32":
		__A						=[]
		__A						={
		    b'\xe0H': KEYMAP['up'] - ARROW_KEY_FLAG,
		    b'\x00H': KEYMAP['up'] - ARROW_KEY_FLAG,
		    b'\xe0P': KEYMAP['down'] - ARROW_KEY_FLAG,
		    b'\x00P': KEYMAP['down'] - ARROW_KEY_FLAG,
		    b'\xe0M': KEYMAP['right'] - ARROW_KEY_FLAG,
		    b'\x00M': KEYMAP['right'] - ARROW_KEY_FLAG,
		    b'\xe0K': KEYMAP['left'] - ARROW_KEY_FLAG,
		    b'\x00K': KEYMAP['left'] - ARROW_KEY_FLAG,
		}

for i in range(10):
		__A						=ord(str(i))







def      _UpperCamelCase   (		):







			if os.name == "nt":
						import msvcrt

						UpperCAmelCase__	:       List[Any]   =  """mbcs"""
						# Flush the keyboard buffer
						while msvcrt.kbhit():
									msvcrt.getch()
						if len(UpperCamelCase__       ) == 0:
									# Read the keystroke
									UpperCAmelCase__	:       Tuple   =  msvcrt.getch()

									# If it is a prefix char, get second part
									if ch in (b"\x00", b"\xe0"):
												UpperCAmelCase__	:       Tuple   =  ch + msvcrt.getch()
												# Translate actual Win chars to bullet char types
												try:
															UpperCAmelCase__	:       str   =  chr(WIN_KEYMAP[cha]       )
															WIN_CH_BUFFER.append(chr(KEYMAP["""mod_int"""]       )       )
															WIN_CH_BUFFER.append(UpperCamelCase__       )
															if ord(UpperCamelCase__       ) in (
															    KEYMAP["insert"] - 1 << 9,
															    KEYMAP["delete"] - 1 << 9,
															    KEYMAP["pg_up"] - 1 << 9,
															    KEYMAP["pg_down"] - 1 << 9,
															):
																		WIN_CH_BUFFER.append(chr(1_2_6       )       )
															UpperCAmelCase__	:       Dict   =  chr(KEYMAP["""esc"""]       )
												except KeyError:
															UpperCAmelCase__	:       Union[str, Any]   =  cha[1]
									else:
												UpperCAmelCase__	:       Dict   =  ch.decode(UpperCamelCase__       )
						else:
									UpperCAmelCase__	:       Union[str, Any]   =  WIN_CH_BUFFER.pop(0       )
			elif os.name == "posix":
						import termios
						import tty

						UpperCAmelCase__	:       Dict   =  sys.stdin.fileno()
						UpperCAmelCase__	:       int   =  termios.tcgetattr(UpperCamelCase__       )
						try:
									tty.setraw(UpperCamelCase__       )
									UpperCAmelCase__	:       Any   =  sys.stdin.read(1       )
						finally:
									termios.tcsetattr(UpperCamelCase__			,   termios.TCSADRAIN			,   UpperCamelCase__       )
			return ch







def      _UpperCamelCase   (		):
			UpperCAmelCase__	:       int   =  get_raw_chars()
			if ord(UpperCamelCase__       ) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
						return char

			elif ord(UpperCamelCase__       ) == KEYMAP["esc"]:
						UpperCAmelCase__	:       Any   =  get_raw_chars()
						if ord(UpperCamelCase__       ) == KEYMAP["mod_int"]:
									UpperCAmelCase__	:       Any   =  get_raw_chars()
									if ord(UpperCamelCase__       ) >= KEYMAP["arrow_begin"] - ARROW_KEY_FLAG and ord(UpperCamelCase__       ) <= KEYMAP["arrow_end"] - ARROW_KEY_FLAG:
												return chr(ord(UpperCamelCase__       ) + ARROW_KEY_FLAG       )
									else:
												return KEYMAP["undefined"]
						else:
									return get_raw_chars()

			else:
						if char in string.printable:
									return char
						else:
									return KEYMAP["undefined"]





'''simple docstring'''







import os

import torch

from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version


if is_torch_version('''>=''', FSDP_PYTORCH_VERSION):
    import torch.distributed.checkpoint as dist_cp
    from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner, DefaultSavePlanner
    from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict
    from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
    from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType


lowerCAmelCase_     						=				get_logger(__name__)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase=0						):






   '''simple docstring'''







   os.makedirs(UpperCAmelCase      ,exist_ok=UpperCAmelCase						)
   with FSDP.state_dict_type(
       UpperCAmelCase      ,fsdp_plugin.state_dict_type      ,fsdp_plugin.state_dict_config      ,fsdp_plugin.optim_state_dict_config						):
      A__       =     model.state_dict()
      if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
         A__       =     F"""{MODEL_NAME}.bin""" if model_index == 0 else F"""{MODEL_NAME}_{model_index}.bin"""
         A__       =     os.path.join(UpperCAmelCase      ,UpperCAmelCase						)
         if accelerator.process_index == 0:
            logger.info(F"""Saving model to {output_model_file}"""						)
            torch.save(UpperCAmelCase      ,UpperCAmelCase						)
            logger.info(F"""Model saved to {output_model_file}"""						)
      elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
         A__       =     (
             F"""{MODEL_NAME}_rank{accelerator.process_index}.bin"""
             if model_index == 0
             else F"""{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"""
         )
         A__       =     os.path.join(UpperCAmelCase      ,UpperCAmelCase						)
         logger.info(F"""Saving model to {output_model_file}"""						)
         torch.save(UpperCAmelCase      ,UpperCAmelCase						)
         logger.info(F"""Model saved to {output_model_file}"""						)
      elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
         A__       =     os.path.join(UpperCAmelCase      ,F"""{MODEL_NAME}_{model_index}"""						)
         os.makedirs(UpperCAmelCase      ,exist_ok=UpperCAmelCase						)
         logger.info(F"""Saving model to {ckpt_dir}"""						)
         A__       =     {'model': state_dict}

         dist_cp.save_state_dict(
             state_dict=UpperCAmelCase      ,storage_writer=dist_cp.FileSystemWriter(UpperCAmelCase						)      ,planner=DefaultSavePlanner()      ,)
         logger.info(F"""Model saved to {ckpt_dir}"""						)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase=0						):






   '''simple docstring'''







   accelerator.wait_for_everyone()
   with FSDP.state_dict_type(
       UpperCAmelCase      ,fsdp_plugin.state_dict_type      ,fsdp_plugin.state_dict_config      ,fsdp_plugin.optim_state_dict_config						):
      if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
         if type(UpperCAmelCase						) != FSDP and accelerator.process_index != 0:
            if not fsdp_plugin.sync_module_states:
               raise ValueError(
                   'Set the `sync_module_states` flag to `True` so that model states are synced across processes when '
                   'initializing FSDP object'						)
            return
         A__       =     F"""{MODEL_NAME}.bin""" if model_index == 0 else F"""{MODEL_NAME}_{model_index}.bin"""
         A__       =     os.path.join(UpperCAmelCase      ,UpperCAmelCase						)
         logger.info(F"""Loading model from {input_model_file}"""						)
         A__       =     torch.load(UpperCAmelCase						)
         logger.info(F"""Model loaded from {input_model_file}"""						)
      elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
         A__       =     (
             F"""{MODEL_NAME}_rank{accelerator.process_index}.bin"""
             if model_index == 0
             else F"""{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"""
         )
         A__       =     os.path.join(UpperCAmelCase      ,UpperCAmelCase						)
         logger.info(F"""Loading model from {input_model_file}"""						)
         A__       =     torch.load(UpperCAmelCase						)
         logger.info(F"""Model loaded from {input_model_file}"""						)
      elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
         A__       =     (
             os.path.join(UpperCAmelCase      ,F"""{MODEL_NAME}_{model_index}"""						)
             if F"""{MODEL_NAME}""" not in input_dir
             else input_dir
         )
         logger.info(F"""Loading model from {ckpt_dir}"""						)
         A__       =     {'model': model.state_dict()}
         dist_cp.load_state_dict(
             state_dict=UpperCAmelCase      ,storage_reader=dist_cp.FileSystemReader(UpperCAmelCase						)      ,planner=DefaultLoadPlanner()      ,)
         A__       =     state_dict['model']
         logger.info(F"""Model loaded from {ckpt_dir}"""						)
      model.load_state_dict(UpperCAmelCase						)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase=0						):






   '''simple docstring'''







   os.makedirs(UpperCAmelCase      ,exist_ok=UpperCAmelCase						)
   with FSDP.state_dict_type(
       UpperCAmelCase      ,fsdp_plugin.state_dict_type      ,fsdp_plugin.state_dict_config      ,fsdp_plugin.optim_state_dict_config						):
      A__       =     FSDP.optim_state_dict(UpperCAmelCase      ,UpperCAmelCase						)
      if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
         if accelerator.process_index == 0:
            A__       =     (
                F"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else F"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
            )
            A__       =     os.path.join(UpperCAmelCase      ,UpperCAmelCase						)
            logger.info(F"""Saving Optimizer state to {output_optimizer_file}"""						)
            torch.save(UpperCAmelCase      ,UpperCAmelCase						)
            logger.info(F"""Optimizer state saved in {output_optimizer_file}"""						)
      else:
         A__       =     os.path.join(UpperCAmelCase      ,F"""{OPTIMIZER_NAME}_{optimizer_index}"""						)
         os.makedirs(UpperCAmelCase      ,exist_ok=UpperCAmelCase						)
         logger.info(F"""Saving Optimizer state to {ckpt_dir}"""						)
         dist_cp.save_state_dict(
             state_dict={'optimizer': optim_state}      ,storage_writer=dist_cp.FileSystemWriter(UpperCAmelCase						)      ,planner=DefaultSavePlanner()      ,)
         logger.info(F"""Optimizer state saved in {ckpt_dir}"""						)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase=0						):






   '''simple docstring'''







   accelerator.wait_for_everyone()
   with FSDP.state_dict_type(
       UpperCAmelCase      ,fsdp_plugin.state_dict_type      ,fsdp_plugin.state_dict_config      ,fsdp_plugin.optim_state_dict_config						):
      if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
         A__       =     None
         # below check should work but currently it isn't working (mostly opytorch issue),
         # in the meantime disabling it at the cost of excess memory usage
         # if accelerator.process_index == 0 or not fsdp_plugin.optim_state_dict_config.rank0_only:
         A__       =     (
             F"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else F"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
         )
         A__       =     os.path.join(UpperCAmelCase      ,UpperCAmelCase						)
         logger.info(F"""Loading Optimizer state from {input_optimizer_file}"""						)
         A__       =     torch.load(UpperCAmelCase						)
         logger.info(F"""Optimizer state loaded from {input_optimizer_file}"""						)
      else:
         A__       =     (
             os.path.join(UpperCAmelCase      ,F"""{OPTIMIZER_NAME}_{optimizer_index}"""						)
             if F"""{OPTIMIZER_NAME}""" not in input_dir
             else input_dir
         )
         logger.info(F"""Loading Optimizer from {ckpt_dir}"""						)
         A__       =     load_sharded_optimizer_state_dict(
             model_state_dict=model.state_dict()      ,optimizer_key='optimizer'      ,storage_reader=dist_cp.FileSystemReader(UpperCAmelCase						)      ,)
         A__       =     optim_state['optimizer']
         logger.info(F"""Optimizer loaded from {ckpt_dir}"""						)
      A__       =     FSDP.optim_state_dict_to_load(UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						)
      optimizer.load_state_dict(UpperCAmelCase						)


